Predictive Modeling of Environmental Fines Using Historical Accounting Data (gjstudies, 2024)
This paper introduces a novel methodological framework for predicting corporateenvironmental fines by leveraging historical accounting data, a previously underexplored data source in environmental compliance forecasting. Traditional approachesto environmental risk assessment have relied heavily on direct environmental metrics,regulatory history, and industry-specific factors, often overlooking the rich predictivesignals embedded in financial statements. We propose that patterns in accountingdata—including expense allocations, capital expenditure trends, depreciation methods,and footnote disclosures—contain latent indicators of environmental management priorities and potential compliance vulnerabilities. Our research formulates the predictionof environmental fines as a time-series classification problem, employing a hybrid modelthat combines autoregressive integrated moving average (ARIMA) components for capturing temporal financial trends with a multilayer perceptron for capturing non-linearinteractions between accounting variables. We construct a unique dataset spanning1998 to 2004, linking the financial statements of manufacturing firms from Compustatwith environmental penalty records from the EPA’s Enforcement and Compliance History Online (ECHO) database. The model identifies several key accounting predictors,most notably the ratio of repair and maintenance expenses to capital expenditures, thevolatility of cost of goods sold, and specific linguistic cues in management discussionand analysis (MDA) sections related to environmental contingencies. Results demonstrate a predictive accuracy of 82.7% in classifying firms that will incur a significantenvironmental fine within the next fiscal year, a substantial improvement over baselinemodels using only environmental performance indicators. This work establishes a new,cross-disciplinary research direction at the intersection of environmental informatics,forensic accounting, and predictive analytics, offering regulators and investors a proactive tool for risk assessment derived from routinely published financial information.